Difference between revisions of "Slicer4:VectorImageVisualization"
From Slicer Wiki
| Line 45: | Line 45: | ||
=Use Case Scenario= | =Use Case Scenario= | ||
| − | *RECIST | + | *R: RECIST analysis |
| − | *Time Series from an MS Patient | + | *M: Time Series Analysis of MRI's from an MS Patient |
| − | *Time Series of MR/CT PET | + | *P: Time Series Analysis of SUV values in MR/CT PET |
| − | == | + | ==Common Features and Needs== |
| − | *time series of scalar volumes | + | *These are all time series of scalar volumes |
| − | * | + | *These MVC's all have Multiple "channels" per time point. Examples: R[pre-, post contrast], M[T1, PD, T2], P[CT, PET] |
| − | * | + | *Typically the volumes need to be registered to each other: Fusion between the channels, fusion across the time points |
| − | * | + | *bias correction is often needed: MR, PET, CT(?) |
| − | * | + | *window/leveling: CT: one for all, PET one for all after SUV, MR one for all after histogram equalization across time inside a ROI. Allow manual tweaking |
| − | + | ===Viewing and Analysis=== | |
| − | *view across time: movie (loop, bounce) | + | *view across time: movie (loop, bounce) |
| + | *graphing: resample across time | ||
*need to do analysis across time: ROI's (label map) for voxel statistics across time. Create spreadsheet / graph as result. | *need to do analysis across time: ROI's (label map) for voxel statistics across time. Create spreadsheet / graph as result. | ||
*need to store the organizational frame work. When the patient comes for the next scan we need ability to add a time point, fix data (registration, w/l, update spreadsheet/graph) | *need to store the organizational frame work. When the patient comes for the next scan we need ability to add a time point, fix data (registration, w/l, update spreadsheet/graph) | ||
Revision as of 16:08, 20 March 2011
Home < Slicer4:VectorImageVisualizationThe main goal of this page is to set the grounds and goals for a common visualization framework for vector images.
Contents
Description of vector images
We consider vector-valued images for the purpose of this project any image composed by several scalar images in which the dimensions and spatial transforms are the same.
Examples of these are:
- DCM-MRI / functional MRI, nowadays handled by Slicer using Junichi's FourDAnalysis module
- Deformation fields which are the result of a registration process.
- DWI-MRI, currently handled by Slicer.
- DTI-Images could be set under this category but their visualization use-cases are, maybe, far too different from the others.
- More complex representations of diffusion MRI (DSI, Q-Ball, Multishell)
| Image type | 4th axis semantics | Number of samples on the 4th axis |
|---|---|---|
| DCM-MRI / fMRI | Time | not fixed |
| Multi-Channel Data | Contrast | not fixed |
| Deformation field | X,Y,Z components of the transform | 3 |
| DWI-MRI | a function with domain angle x b-value | not fixed |
| DTI-MRI | Components of a symmetric positive definite matrix | 9 (or 6 as there are only 6 independent components) |
| More complex representations of diffusion MRI | spherical harmonic coefficients, angular functions, etc | not fixed |
Basic functionalities
- Multi-component scalar-volume visualization: as with the DWI-images where there is a slider enabling the user to choose which component to visualize
- Split and merge into individual volumes. Concept is partially implemented in split and merge in the interactive editor
- Visualization of derived values:
- dMRI: Glyph-based visualization of tensors, scalar visualization of color by orientation, fractional anisotropy
- Visualization as vectors (deformation fields, principal diffusion direction)
- Visualization as Ellipsoids or multiquadrics (representation of DT-MRI)
- DCE: visualization of the parameter values extracted from model fitting such as tofts model.
- dMRI: Glyph-based visualization of tensors, scalar visualization of color by orientation, fractional anisotropy
- 2D-plot exploring of a single voxel along the 4th axis as in FourDAnalysis module
- Colormap visualization: Mapping from a 3D vector to the RGB components. Examples are
- DT-MRI visualization
- Direction of the deformation field at each voxel
- combinations of multichannel volumes (microscopy)
Use Case Scenario
- R: RECIST analysis
- M: Time Series Analysis of MRI's from an MS Patient
- P: Time Series Analysis of SUV values in MR/CT PET
Common Features and Needs
- These are all time series of scalar volumes
- These MVC's all have Multiple "channels" per time point. Examples: R[pre-, post contrast], M[T1, PD, T2], P[CT, PET]
- Typically the volumes need to be registered to each other: Fusion between the channels, fusion across the time points
- bias correction is often needed: MR, PET, CT(?)
- window/leveling: CT: one for all, PET one for all after SUV, MR one for all after histogram equalization across time inside a ROI. Allow manual tweaking
Viewing and Analysis
- view across time: movie (loop, bounce)
- graphing: resample across time
- need to do analysis across time: ROI's (label map) for voxel statistics across time. Create spreadsheet / graph as result.
- need to store the organizational frame work. When the patient comes for the next scan we need ability to add a time point, fix data (registration, w/l, update spreadsheet/graph)
Workflows
Initial Setup of a MultiVolumeContainer
- Assume that all data have been loaded into Slicer already
- Create a new MultiVolumeContainer.
- MVC's are always multi-channel. Single channel is just a special case.
- User specifies the number of channels.
- one of the volumes is assigned as the reference volume (default is the first loaded, user can change manually by clicking a checkbox)
Things you can do with MVC's
- editing the MVC by adding or removing time points or changing the order manually
- change the reference volume
- assign registration types between the channels and between the time points
- execute the registration cascade
- adjust W/L across time for each channel using histogram equalization with manual override
- across time analysis: label map with multiple ROI's will give across time statistics
- special case for SUV across time
- RECIST: determination at each time point, use previous one as initialization
- Formulas: DCE requires Tofts, NukMed uses other, similar formulas, DWI to DTI uses Stejskal.
